import pandas as pd
import os
import us
from us import states
import plotly.graph_objects as go
import plotly.express as px
import requests
import dotenv
os.chdir('C:\\Users\\nguye\\Documents\\UVA\\Term 3\\Bayesian Machine Learning\\Project\\Data')
os.getcwd()
'C:\\Users\\nguye\\Documents\\UVA\\Term 3\\Bayesian Machine Learning\\Project\\Data'
dotenv.load_dotenv('.env')
True
censuskey = os.getenv('censuskey')
censuskey
'33b860c51f0e598ef29489986b5b936ee248a38b'
cdc_lyme = pd.read_csv('LD-Case-Counts-by-County-00-19.csv', encoding = 'latin1')
cdc_lyme
| Ctyname | Stname | STCODE | CTYCODE | Cases2000 | Cases2001 | Cases2002 | Cases2003 | Cases2004 | Cases2005 | ... | Cases2010 | Cases2011 | Cases2012 | Cases2013 | Cases2014 | Cases2015 | Cases2016 | Cases2017 | Cases2018 | Cases2019 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Autauga County | Alabama | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 |
| 1 | Baldwin County | Alabama | 1 | 3 | 1 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 1 | 1 | 0 | 3 | 1 | 2 | 2 | 0 | 0 |
| 2 | Barbour County | Alabama | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 3 | Bibb County | Alabama | 1 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 |
| 4 | Blount County | Alabama | 1 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3188 | Teton County | Wyoming | 56 | 39 | 0 | 0 | 1 | 0 | 1 | 1 | ... | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 2 | 0 | 0 |
| 3189 | Uinta County | Wyoming | 56 | 41 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3190 | Washakie County | Wyoming | 56 | 43 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3191 | Weston County | Wyoming | 56 | 45 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3192 | Wyoming | Wyoming | 56 | 999 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 3 |
3193 rows × 24 columns
Per the CDC, code '999' represents unknown county within a single state so we'll compare the total number cases per county each year and see how much of it represents county.
cdc_lyme[cdc_lyme['CTYCODE'] == '999']
| Ctyname | Stname | STCODE | CTYCODE | Cases2000 | Cases2001 | Cases2002 | Cases2003 | Cases2004 | Cases2005 | ... | Cases2010 | Cases2011 | Cases2012 | Cases2013 | Cases2014 | Cases2015 | Cases2016 | Cases2017 | Cases2018 | Cases2019 |
|---|
0 rows × 24 columns
cdc_lyme[cdc_lyme['CTYCODE'] != '999']
| Ctyname | Stname | STCODE | CTYCODE | Cases2000 | Cases2001 | Cases2002 | Cases2003 | Cases2004 | Cases2005 | ... | Cases2010 | Cases2011 | Cases2012 | Cases2013 | Cases2014 | Cases2015 | Cases2016 | Cases2017 | Cases2018 | Cases2019 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Autauga County | Alabama | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 |
| 1 | Baldwin County | Alabama | 1 | 3 | 1 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 1 | 1 | 0 | 3 | 1 | 2 | 2 | 0 | 0 |
| 2 | Barbour County | Alabama | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 3 | Bibb County | Alabama | 1 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 |
| 4 | Blount County | Alabama | 1 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3188 | Teton County | Wyoming | 56 | 39 | 0 | 0 | 1 | 0 | 1 | 1 | ... | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 2 | 0 | 0 |
| 3189 | Uinta County | Wyoming | 56 | 41 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3190 | Washakie County | Wyoming | 56 | 43 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3191 | Weston County | Wyoming | 56 | 45 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3192 | Wyoming | Wyoming | 56 | 999 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 3 |
3193 rows × 24 columns
cdc_lyme['CTYCODE'] = cdc_lyme['CTYCODE'].astype(str).str.zfill(3)
cdc_lyme['STCODE'] = cdc_lyme['STCODE'].astype(str).str.zfill(2)
cdc_lyme['FIPS'] = cdc_lyme['STCODE'] + cdc_lyme['CTYCODE']
cdc_lyme['stabbr'] = cdc_lyme['STCODE'].map(us.states.mapping('fips', 'abbr'))
state_abbr = cdc_lyme['stabbr'].unique()
state_count_2000 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2000'].sum() for st in state_abbr]
state_count_2001 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2001'].sum() for st in state_abbr]
state_count_2002 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2002'].sum() for st in state_abbr]
state_count_2003 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2003'].sum() for st in state_abbr]
state_count_2004 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2004'].sum() for st in state_abbr]
state_count_2005 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2005'].sum() for st in state_abbr]
state_count_2016 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2006'].sum() for st in state_abbr]
state_count_2007 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2007'].sum() for st in state_abbr]
state_count_2008 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2008'].sum() for st in state_abbr]
state_count_2009 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2009'].sum() for st in state_abbr]
state_count_2010 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2010'].sum() for st in state_abbr]
state_count_2011 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2011'].sum() for st in state_abbr]
state_count_2012 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2012'].sum() for st in state_abbr]
state_count_2013 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2013'].sum() for st in state_abbr]
state_count_2014 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2014'].sum() for st in state_abbr]
state_count_2015 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2015'].sum() for st in state_abbr]
state_count_2016 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2016'].sum() for st in state_abbr]
state_count_2017 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2017'].sum() for st in state_abbr]
state_count_2018 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2018'].sum() for st in state_abbr]
state_count_2019 = [cdc_lyme[cdc_lyme['stabbr'] == st]['Cases2019'].sum() for st in state_abbr]
cases_year = ['Cases2000', 'Cases2001',
'Cases2002', 'Cases2003', 'Cases2004', 'Cases2005', 'Cases2006',
'Cases2007', 'Cases2008', 'Cases2009', 'Cases2010', 'Cases2011',
'Cases2012', 'Cases2013', 'Cases2014', 'Cases2015', 'Cases2016',
'Cases2017', 'Cases2018', 'Cases2019']
sum = []
count_summary = pd.DataFrame({'State': state_abbr,
'2000': state_count_2000,
'2001': state_count_2001,
'2002': state_count_2002,
'2003': state_count_2003,
'2004': state_count_2004,
'2005': state_count_2005,
'2006': state_count_2016,
'2007': state_count_2007,
'2008': state_count_2008,
'2009': state_count_2009,
'2010': state_count_2010,
'2011': state_count_2011,
'2012': state_count_2012,
'2013': state_count_2013,
'2014': state_count_2014,
'2015': state_count_2015,
'2016': state_count_2016,
'2017': state_count_2017,
'2018': state_count_2018,
'2019': state_count_2019})
count_summary
| State | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | ... | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AL | 6 | 10 | 11 | 8 | 6 | 3 | 38 | 13 | 9 | ... | 2 | 24 | 25 | 24 | 64 | 25 | 38 | 41 | 36 | 66 |
| 1 | AK | 2 | 2 | 3 | 3 | 3 | 4 | 15 | 10 | 6 | ... | 7 | 11 | 10 | 14 | 8 | 9 | 15 | 10 | 11 | 3 |
| 2 | AZ | 2 | 3 | 4 | 4 | 13 | 10 | 13 | 2 | 8 | ... | 2 | 15 | 13 | 32 | 21 | 12 | 13 | 28 | 7 | 10 |
| 3 | AR | 7 | 4 | 3 | 0 | 0 | 0 | 2 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 6 | 4 | 18 |
| 4 | CA | 96 | 95 | 97 | 86 | 48 | 95 | 134 | 75 | 74 | ... | 129 | 92 | 70 | 112 | 73 | 98 | 134 | 145 | 104 | 144 |
| 5 | CO | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | ... | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 3 | 8 |
| 6 | CT | 3773 | 3597 | 4631 | 1403 | 1348 | 1810 | 1748 | 3058 | 3896 | ... | 3068 | 3039 | 2657 | 2925 | 2360 | 2541 | 1748 | 2051 | 1859 | 1233 |
| 7 | DE | 167 | 152 | 194 | 212 | 339 | 646 | 506 | 715 | 772 | ... | 656 | 873 | 669 | 509 | 417 | 435 | 506 | 608 | 520 | 641 |
| 8 | DC | 11 | 17 | 25 | 14 | 16 | 10 | 103 | 116 | 74 | ... | 42 | 0 | 0 | 35 | 40 | 121 | 103 | 84 | 79 | 100 |
| 9 | FL | 54 | 43 | 79 | 43 | 46 | 47 | 216 | 30 | 88 | ... | 84 | 115 | 118 | 138 | 155 | 166 | 216 | 210 | 169 | 162 |
| 10 | GA | 0 | 0 | 2 | 10 | 12 | 6 | 4 | 11 | 35 | ... | 10 | 32 | 31 | 8 | 4 | 8 | 4 | 8 | 19 | 18 |
| 11 | HI | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | ID | 4 | 5 | 4 | 3 | 6 | 2 | 17 | 9 | 9 | ... | 9 | 4 | 5 | 19 | 9 | 9 | 17 | 20 | 9 | 14 |
| 13 | IL | 35 | 32 | 47 | 71 | 87 | 127 | 237 | 149 | 108 | ... | 135 | 194 | 204 | 337 | 233 | 287 | 237 | 273 | 276 | 395 |
| 14 | IN | 23 | 26 | 21 | 25 | 32 | 33 | 152 | 55 | 42 | ... | 78 | 94 | 74 | 110 | 110 | 138 | 152 | 142 | 155 | 189 |
| 15 | IA | 34 | 36 | 42 | 58 | 49 | 89 | 232 | 123 | 109 | ... | 85 | 100 | 165 | 247 | 194 | 318 | 232 | 255 | 283 | 303 |
| 16 | KS | 17 | 2 | 7 | 4 | 3 | 3 | 39 | 8 | 16 | ... | 10 | 17 | 19 | 34 | 20 | 23 | 39 | 40 | 30 | 35 |
| 17 | KY | 13 | 23 | 25 | 17 | 15 | 5 | 33 | 6 | 5 | ... | 5 | 3 | 14 | 40 | 44 | 49 | 33 | 20 | 22 | 22 |
| 18 | LA | 8 | 8 | 5 | 7 | 2 | 3 | 7 | 2 | 3 | ... | 3 | 2 | 7 | 0 | 2 | 3 | 7 | 12 | 4 | 8 |
| 19 | ME | 71 | 108 | 219 | 175 | 225 | 247 | 1487 | 529 | 908 | ... | 751 | 1006 | 1111 | 1373 | 1401 | 1201 | 1487 | 1850 | 1405 | 2167 |
| 20 | MD | 688 | 608 | 738 | 691 | 891 | 1235 | 1866 | 2576 | 2218 | ... | 1617 | 1351 | 1651 | 1197 | 1373 | 1728 | 1866 | 1891 | 1382 | 1417 |
| 21 | MA | 1158 | 1164 | 1807 | 1532 | 1532 | 2336 | 198 | 2988 | 4582 | ... | 3263 | 2476 | 5138 | 5290 | 5304 | 4224 | 198 | 410 | 16 | 7 |
| 22 | MI | 23 | 21 | 26 | 12 | 27 | 62 | 221 | 51 | 92 | ... | 95 | 104 | 98 | 168 | 127 | 148 | 221 | 291 | 262 | 413 |
| 23 | MN | 465 | 461 | 867 | 474 | 1023 | 917 | 2126 | 1238 | 1282 | ... | 1960 | 2124 | 1515 | 2340 | 1416 | 1805 | 2126 | 2318 | 1541 | 1528 |
| 24 | MS | 3 | 8 | 12 | 21 | 0 | 0 | 1 | 1 | 1 | ... | 0 | 5 | 1 | 0 | 2 | 4 | 1 | 1 | 4 | 4 |
| 25 | MO | 47 | 37 | 41 | 70 | 25 | 15 | 10 | 10 | 6 | ... | 4 | 8 | 2 | 3 | 10 | 5 | 10 | 12 | 11 | 17 |
| 26 | MT | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 4 | 17 | ... | 4 | 11 | 6 | 18 | 7 | 5 | 17 | 12 | 7 | 8 |
| 27 | NE | 5 | 4 | 6 | 2 | 2 | 2 | 14 | 7 | 12 | ... | 8 | 11 | 15 | 10 | 7 | 11 | 14 | 14 | 15 | 10 |
| 28 | NV | 4 | 4 | 2 | 3 | 1 | 3 | 15 | 15 | 12 | ... | 2 | 5 | 10 | 16 | 6 | 7 | 15 | 17 | 14 | 17 |
| 29 | NH | 84 | 129 | 261 | 190 | 226 | 265 | 891 | 896 | 1601 | ... | 1339 | 1299 | 1450 | 1687 | 724 | 529 | 891 | 1381 | 1428 | 1710 |
| 30 | NJ | 2459 | 2020 | 2349 | 2887 | 2698 | 3363 | 4350 | 3134 | 3485 | ... | 3712 | 4262 | 3576 | 3766 | 3286 | 4855 | 4350 | 5092 | 4000 | 3619 |
| 31 | NM | 0 | 1 | 1 | 1 | 1 | 3 | 1 | 5 | 8 | ... | 5 | 6 | 1 | 6 | 0 | 0 | 1 | 3 | 2 | 7 |
| 32 | NY | 4329 | 4083 | 5535 | 5399 | 5100 | 5565 | 3882 | 4165 | 7794 | ... | 3425 | 4490 | 2998 | 4615 | 3736 | 4314 | 3882 | 5155 | 3638 | 4243 |
| 33 | NC | 47 | 41 | 137 | 156 | 122 | 49 | 272 | 53 | 47 | ... | 82 | 88 | 122 | 180 | 170 | 230 | 272 | 295 | 212 | 334 |
| 34 | ND | 2 | 0 | 1 | 0 | 0 | 3 | 32 | 12 | 10 | ... | 33 | 27 | 15 | 29 | 14 | 33 | 32 | 56 | 33 | 38 |
| 35 | OH | 61 | 44 | 82 | 66 | 50 | 58 | 160 | 33 | 45 | ... | 44 | 53 | 67 | 93 | 119 | 154 | 160 | 270 | 293 | 467 |
| 36 | OK | 1 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 2 | ... | 0 | 2 | 4 | 3 | 0 | 0 | 0 | 1 | 0 | 0 |
| 37 | OR | 13 | 15 | 12 | 16 | 11 | 3 | 61 | 6 | 38 | ... | 39 | 38 | 48 | 43 | 45 | 31 | 61 | 84 | 70 | 65 |
| 38 | PA | 2343 | 2806 | 3989 | 5730 | 3985 | 4287 | 11443 | 3994 | 3818 | ... | 3805 | 5362 | 5033 | 5758 | 7487 | 9048 | 11443 | 11900 | 10208 | 8998 |
| 39 | RI | 675 | 510 | 852 | 736 | 249 | 39 | 903 | 177 | 210 | ... | 181 | 159 | 217 | 724 | 904 | 904 | 903 | 1132 | 1111 | 971 |
| 40 | SC | 25 | 6 | 26 | 18 | 22 | 15 | 51 | 31 | 29 | ... | 29 | 37 | 44 | 42 | 37 | 42 | 51 | 21 | 39 | 47 |
| 41 | SD | 0 | 0 | 2 | 1 | 1 | 2 | 11 | 0 | 3 | ... | 1 | 4 | 4 | 4 | 2 | 5 | 11 | 12 | 7 | 10 |
| 42 | TN | 28 | 31 | 28 | 20 | 20 | 8 | 25 | 31 | 31 | ... | 36 | 37 | 30 | 25 | 17 | 26 | 25 | 47 | 29 | 45 |
| 43 | TX | 77 | 75 | 139 | 85 | 98 | 69 | 71 | 87 | 153 | ... | 142 | 74 | 75 | 82 | 40 | 54 | 71 | 58 | 47 | 41 |
| 44 | UT | 3 | 1 | 5 | 2 | 1 | 2 | 19 | 7 | 5 | ... | 3 | 9 | 5 | 15 | 13 | 7 | 19 | 25 | 27 | 19 |
| 45 | VT | 40 | 18 | 37 | 43 | 50 | 54 | 761 | 138 | 404 | ... | 356 | 623 | 522 | 893 | 599 | 710 | 761 | 1092 | 576 | 1064 |
| 46 | VA | 149 | 156 | 259 | 195 | 216 | 274 | 1350 | 959 | 933 | ... | 1245 | 1023 | 1110 | 1307 | 1346 | 1539 | 1350 | 1657 | 1139 | 1199 |
| 47 | WA | 9 | 9 | 11 | 7 | 14 | 13 | 31 | 12 | 23 | ... | 16 | 19 | 15 | 18 | 15 | 24 | 31 | 37 | 20 | 43 |
| 48 | WV | 35 | 16 | 26 | 31 | 38 | 61 | 368 | 84 | 135 | ... | 145 | 118 | 97 | 143 | 136 | 289 | 368 | 648 | 671 | 885 |
| 49 | WI | 631 | 597 | 1090 | 740 | 1144 | 1459 | 2295 | 1814 | 2034 | ... | 3488 | 3649 | 1766 | 1872 | 1361 | 1894 | 2295 | 3000 | 1869 | 2178 |
| 50 | WY | 3 | 1 | 2 | 2 | 4 | 3 | 1 | 3 | 3 | ... | 0 | 2 | 4 | 3 | 3 | 1 | 1 | 4 | 0 | 5 |
51 rows × 21 columns
count_summary_melted = pd.melt(count_summary, id_vars = ['State'], value_vars = [str(i) for i in range(2000, 2019)])
count_summary_melted.rename(columns = {'variable': 'Year', 'value': 'Count'}, inplace = True)
count_summary_melted
| State | Year | Count | |
|---|---|---|---|
| 0 | AL | 2000 | 6 |
| 1 | AK | 2000 | 2 |
| 2 | AZ | 2000 | 2 |
| 3 | AR | 2000 | 7 |
| 4 | CA | 2000 | 96 |
| ... | ... | ... | ... |
| 964 | VA | 2018 | 1139 |
| 965 | WA | 2018 | 20 |
| 966 | WV | 2018 | 671 |
| 967 | WI | 2018 | 1869 |
| 968 | WY | 2018 | 0 |
969 rows × 3 columns
count_summary_melted['Year'] = pd.to_datetime(count_summary_melted['Year']).dt.year
count_summary_melted
| State | Year | Count | |
|---|---|---|---|
| 0 | AL | 2000 | 6 |
| 1 | AK | 2000 | 2 |
| 2 | AZ | 2000 | 2 |
| 3 | AR | 2000 | 7 |
| 4 | CA | 2000 | 96 |
| ... | ... | ... | ... |
| 964 | VA | 2018 | 1139 |
| 965 | WA | 2018 | 20 |
| 966 | WV | 2018 | 671 |
| 967 | WI | 2018 | 1869 |
| 968 | WY | 2018 | 0 |
969 rows × 3 columns
fig = px.choropleth(count_summary_melted,
locations = count_summary_melted['State'],
color = count_summary_melted['Count'],
hover_name = count_summary_melted['State'],
animation_frame = count_summary_melted['Year'],
locationmode = 'USA-states', scope = 'usa',
height = 800)
fig.show()
root = 'https://api.census.gov/data'
year = '2000'
dataset = 'dec/sf1'
url = '/'.join([root, year, dataset])
url
'https://api.census.gov/data/2000/dec/sf1'
predicates = {'get': ['P001001', 'NAME'],
'for': 'states:*',
'key': censuskey}
r = requests.get(url, params = predicates)
predicates
{'get': ['P001001', 'NAME'],
'for': 'states:*',
'key': '33b860c51f0e598ef29489986b5b936ee248a38b'}
r.text
'error: unknown/unsupported geography heirarchy'